Field
This invention relates to a signal encoder comprising an input for receiving a signal comprising frames, each frame comprising sequential samples, and an output for providing a encoded signal, the signal encoder further comprising a segmenter comprising an input for receiving the signal and being arranged for segmenting the sequential samples of a frame into segments comprising n sequential samples, an approximator comprising an input for receiving segments from the segmenter and seed values and an output for providing an encoded signal comprising for each segment a set of predictor model parameters to the output of the encoder, the approximator being arranged to approximate a first segment starting from a first seed sample having a first seed value and determine a first set of predictor model parameters by approximating the n sequential samples of the first segment using a first predictor model and subsequently to approximate a second segment, subsequent to the first segment, starting from a second seed sample having a second seed value and determine a second set of predictor model parameters by approximating the n sequential samples of the second segment using a second predictor model.
Description of the Related Art
Such signal encoder are known from “An application of the piecewise autoregressive model in lossless audio coding” by Yinghua Yang et al, Norsig 2006, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.330.2413&rep=rep1&type=pdf.
A disadvantage of such an encoder is that for each segment a seed value has to obtained, and this achieved by predicting the very first samples of the current frame using samples from the previous frame. This however leads to a build up of the prediction error.